private int FindClosestClusterCenter(List <Centeroid <T> > clusterCenter, DocumentVector <T> obj)
{
float[] similarityMeasure = new float[clusterCenter.Count()];
for (int i = 0; i < clusterCenter.Count(); i++)
{
similarityMeasure[i] = SimilarityMatrics.FindCosineSimilarity(clusterCenter[i].GroupedDocument[0].VectorSpace, obj.VectorSpace);
}
int index = 0;
float maxValue = similarityMeasure[0];
for (int i = 0; i < similarityMeasure.Count(); i++)
{
if (similarityMeasure[i] > maxValue)
{
maxValue = similarityMeasure[i];
index = i;
}
}
return(index);
}
private int FindClosestClusterCenter(List <Centeroid <T> > clusterCenter, DocumentVector <T> docVector)
{
float[] similarityMeasure = new float[clusterCenter.Count];
int index = 0;
float maxValue = similarityMeasure[0];
Parallel.For(0, clusterCenter.Count, i =>
{
if (clusterCenter[i].GroupedDocument.Count > 0)
{
similarityMeasure[i] = SimilarityMatrics
.FindCosineSimilarity(clusterCenter[i].GroupedDocument[0].VectorSpace, docVector.VectorSpace);
}
if (similarityMeasure[i] > maxValue)
{
maxValue = similarityMeasure[i];
index = i;
}
});
return(index);
}
public static string FindClosestDocument()
{
if (centroid != null)
{
//int countCenter = centroid.GroupedDocument.Count() - 1;
float max = 0;
string doc = "";
// Tính độ tương tự của document với các center rồi add vào mảng similarityMeasure
foreach (var ce in centroid.GroupedDocument)
{
if (ce.Content != documentVector.Content)
{
float current = SimilarityMatrics.FindCosineSimilarity(ce.VectorSpace, documentVector.VectorSpace);
if (max < current)
{
max = current;
doc = ce.Content;
}
}
}
return(doc);
}
return(null);
}